Distributional and Knowledge-Based Approaches for Computing Portuguese Word Similarity
Identifying similar and related words is not only key in natural language understanding but also a suitable task for assessing the quality of computational resources that organise words and meanings of a language, compiled by different means. This paper, which aims to be a reference for those intere...
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doaj-81693e5dec8e4a528299a2d1ef98100c2020-11-24T23:07:39ZengMDPI AGInformation2078-24892018-02-01923510.3390/info9020035info9020035Distributional and Knowledge-Based Approaches for Computing Portuguese Word SimilarityHugo Gonçalo Oliveira0Centre for Informatics and Systems of the University of Coimbra (CISUC), Department of Informatics Engineering, University of Coimbra, 3030-290 Coimbra, PortugalIdentifying similar and related words is not only key in natural language understanding but also a suitable task for assessing the quality of computational resources that organise words and meanings of a language, compiled by different means. This paper, which aims to be a reference for those interested in computing word similarity in Portuguese, presents several approaches for this task and is motivated by the recent availability of state-of-the-art distributional models of Portuguese words, which add to several lexical knowledge bases (LKBs) for this language, available for a longer time. The previous resources were exploited to answer word similarity tests, which also became recently available for Portuguese. We conclude that there are several valid approaches for this task, but not one that outperforms all the others in every single test. Distributional models seem to capture relatedness better, while LKBs are better suited for computing genuine similarity, but, in general, better results are obtained when knowledge from different sources is combined.http://www.mdpi.com/2078-2489/9/2/35semantic similarityword similaritylexical knowledge baseslexical semanticsword embeddingsdistributional semantics |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hugo Gonçalo Oliveira |
spellingShingle |
Hugo Gonçalo Oliveira Distributional and Knowledge-Based Approaches for Computing Portuguese Word Similarity Information semantic similarity word similarity lexical knowledge bases lexical semantics word embeddings distributional semantics |
author_facet |
Hugo Gonçalo Oliveira |
author_sort |
Hugo Gonçalo Oliveira |
title |
Distributional and Knowledge-Based Approaches for Computing Portuguese Word Similarity |
title_short |
Distributional and Knowledge-Based Approaches for Computing Portuguese Word Similarity |
title_full |
Distributional and Knowledge-Based Approaches for Computing Portuguese Word Similarity |
title_fullStr |
Distributional and Knowledge-Based Approaches for Computing Portuguese Word Similarity |
title_full_unstemmed |
Distributional and Knowledge-Based Approaches for Computing Portuguese Word Similarity |
title_sort |
distributional and knowledge-based approaches for computing portuguese word similarity |
publisher |
MDPI AG |
series |
Information |
issn |
2078-2489 |
publishDate |
2018-02-01 |
description |
Identifying similar and related words is not only key in natural language understanding but also a suitable task for assessing the quality of computational resources that organise words and meanings of a language, compiled by different means. This paper, which aims to be a reference for those interested in computing word similarity in Portuguese, presents several approaches for this task and is motivated by the recent availability of state-of-the-art distributional models of Portuguese words, which add to several lexical knowledge bases (LKBs) for this language, available for a longer time. The previous resources were exploited to answer word similarity tests, which also became recently available for Portuguese. We conclude that there are several valid approaches for this task, but not one that outperforms all the others in every single test. Distributional models seem to capture relatedness better, while LKBs are better suited for computing genuine similarity, but, in general, better results are obtained when knowledge from different sources is combined. |
topic |
semantic similarity word similarity lexical knowledge bases lexical semantics word embeddings distributional semantics |
url |
http://www.mdpi.com/2078-2489/9/2/35 |
work_keys_str_mv |
AT hugogoncalooliveira distributionalandknowledgebasedapproachesforcomputingportuguesewordsimilarity |
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